subset
Create subset of datastore or FileSet
Description
subds = subset(
returns a subset containing reads corresponding to ds
,indices
)indices
. The subset
subds
is of the same type as the input.
if the input
ds
is a datastore, then the outputoutds
is a datastore of the same type.if the input
ds
is aFileSet
,DsFileSet
, orBlockedFileSet
object, then the outputsubds
is also, respectively, aFileSet
,DsFileSet
, orBlockedFileSet
object.
Examples
Create Subset of ImageDatastore
Make an image datastore object and then create a subset of that image datastore.
Create an image datastore imds
for all the image files in a
sample folder. Then, display the Files
property of
imds
.
folders = fullfile(matlabroot,'toolbox','matlab',{'demos','imagesci'}); exts = {'.jpg','.png','.tif'}; imds = imageDatastore(folders,'LabelSource','foldernames','FileExtensions',exts); imds.Files
ans = 8×1 cell array {'...\matlab\toolbox\matlab\demos\cloudCombined.jpg'} {'...\matlab\toolbox\matlab\demos\example.tif' } {'...\matlab\toolbox\matlab\demos\landOcean.jpg' } {'...\matlab\toolbox\matlab\demos\ngc6543a.jpg' } {'...\matlab\toolbox\matlab\demos\street1.jpg' } {'...\matlab\toolbox\matlab\demos\street2.jpg' } {'...\matlab\toolbox\matlab\imagesci\corn.tif' } {'...\matlab\toolbox\matlab\imagesci\peppers.png' }
Create a subset datastore subimds
that contains the first four
files of imds
and examine the Files
property of
subimds
.
indices = 1:4; subimds = subset(imds,indices); subimds.Files
ans = 4×1 cell array {'...\matlab\toolbox\matlab\demos\cloudCombined.jpg'} {'...\matlab\toolbox\matlab\demos\example.tif' } {'...\matlab\toolbox\matlab\demos\landOcean.jpg' } {'...\matlab\toolbox\matlab\demos\ngc6543a.jpg' }
Create Subset Datastore with Randomly Selected Files
Make an image datastore, and then create subset datastore containing only a specified percentage of files, randomly selected from the original datastore.
Create imageDatastore
for all the image files in a sample folder
and display the Files
property. This datastore contains 8
files.
folders = fullfile(matlabroot,'toolbox','matlab',{'demos','imagesci'}); exts = {'.jpg','.png','.tif'}; imds = imageDatastore(folders,'LabelSource','foldernames','FileExtensions',exts); imds.Files
ans = 8×1 cell array {'...\matlab\toolbox\matlab\demos\cloudCombined.jpg'} {'...\matlab\toolbox\matlab\demos\example.tif' } {'...\matlab\toolbox\matlab\demos\landOcean.jpg' } {'...\matlab\toolbox\matlab\demos\ngc6543a.jpg' } {'...\matlab\toolbox\matlab\demos\street1.jpg' } {'...\matlab\toolbox\matlab\demos\street2.jpg' } {'...\matlab\toolbox\matlab\imagesci\corn.tif' } {'...\matlab\toolbox\matlab\imagesci\peppers.png' }
Create a set of indices that represents randomly selected subset containing
60%
of the files.
nFiles = length(imds.Files); RandIndices = randperm(nFiles); nSixtyPercent = round(0.6*nFiles); indices = RandIndices(1:nSixtyPercent)
indices = 8 6 4 5 1
Create a subset datastore submids
using
indices
and examine its Files
property.
subimds = subset(imds,indices); subimds.Files
ans = 5×1 cell array {'...\matlab\toolbox\matlab\imagesci\peppers.png' } {'...\matlab\toolbox\matlab\demos\street2.jpg' } {'...\matlab\toolbox\matlab\demos\ngc6543a.jpg' } {'...\matlab\toolbox\matlab\demos\street1.jpg' } {'...\matlab\toolbox\matlab\demos\cloudCombined.jpg'}
Compare Data Granularities
Compare a coarse-grained partition with a fine-grained subset.
Read all the frames in the video file xylophone.mp4
and construct an ArrayDatastore
object to iterate over it. The resulting object has 141 frames.
v = VideoReader("xylophone.mp4"); allFrames = read(v); arrds = arrayDatastore(allFrames,IterationDimension=4,OutputType="cell",ReadSize=4);
To extract a specific set of adjacent frames, create four coarse-grained partitions of arrds
. Extract the second partition, which has 35 frames.
partds = partition(arrds,4,2); imshow(imtile(partds.readall()))
Extract six nonadjacent frames from arrds
at specified indices using a fine-grained subset.
subds = subset(arrds,[67 79 82 69 89 33]); imshow(imtile(subds.readall()))
Input Arguments
ds
— Input datastore or file-set
matlab.io.Datastore
object | FileSet
object | DsFileSet
object | BlockedFileSet
object
Input datastore or file-set, specified as a datastore
, FileSet
,
DsFileSet
, or
BlockedFileSet
object.
indices
— Indices of files to include in subset
vector of indices | logical vector
Indices of files to include in subset, specified as a vector of indices or a logical vector.
The vector of indices must contain the indices of files to include in the subset
subds
.The logical vector must be of the same length as the number of files in the input
ds
. Thesubset
method creates a subsetsubds
containing files corresponding to the elements in the logical vector that have a value oftrue
.
Elements of indices
must be unique.
Data Types: double
| logical
Extended Capabilities
Thread-Based Environment
Run code in the background using MATLAB® backgroundPool
or accelerate code with Parallel Computing Toolbox™ ThreadPool
.
Usage notes and limitations:
In a thread-based environment, you can use
subset
only with the following datastores:ImageDatastore
objectsCombinedDatastore
,SequentialDatastore
, orTransformedDatastore
objects you create fromImageDatastore
objects by usingcombine
ortransform
You can use
subset
with other datastores if you have Parallel Computing Toolbox™. To do so, run the function using a process-backed parallel pool instead of usingbackgroundPool
orThreadPool
(use eitherProcessPool
orClusterPool
).
For more information, see Run MATLAB Functions in Thread-Based Environment.
Version History
Introduced in R2019a
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